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NOvA is a long-baseline neutrino oscillation experiment. It is optimized tomeasure $\nu_e$ appearance and $\nu_{\mu}$ disappearance at the Far Detector inthe $\nu_{\mu}$ beam produced by the NuMI facility at Fermilab. NOvA uses aconvolutional neural network (CNN) to identify neutrino events in twofunctionally identical liquid scintillator detectors. A different network,called prong-CNN, has been used to classify reconstructed particles in eachevent as either lepton or hadron. Within each event, hits are clustered intoprongs to reconstruct final-state particles and these prongs form the input tothis prong-CNN classifier. Classified particle energies are then used as inputto an electron neutrino energy estimator. Improving the resolution andsystematic robustness of NOvA's energy estimator will improve the sensitivityof the oscillation parameters measurement. This paper describes the methods toidentify particles with prong-CNN and the following approach to estimate$\nu_e$ energy for signal events.

Electron Neutrino Energy Reconstruction in NOvA Using CNN Particle IDs